<span lang="EN-US">A Quick and precise crop leaf disease detection is important to increasing agricultural yield in a sustainable manner. We present a comprehensive overview of recent research in the field of crop leaf disease prediction using image processing (IP), machine learning (ML) and deep learning (DL) techniques in this paper. Using these techniques, crop leaf disease prediction made it possible to get notable accuracies. This article presents a survey of research papers that presented the various methodologies, analyzes them in terms of the dataset, number of images, number of classes, algorithms used, convolutional neural networks (CNN) models employed, and overall performance achieved. Then, suggestions are prepared on the most appropriate algorithms to deploy in standard, mobile/embedded systems, Drones, Robots and unmanned aerial vehicles (UAV). We discussed the performance measures used and listed some of the limitations and future works that requires to be focus on, to extend real time automated crop leaf disease detection system.</span>

As per need of recent applications, new research aspects related to scalability, heterogeneity, and power consumption have been arisen. These problems are supposed to be fixed for better utilization of MANETs. MANET nodes interact through multi-hop routing. AODV is a commonly used on-demand protocol for routing in MANETs. In the existing literature, AODV has been analyzed a number of times but heterogeneity of the nodes has not been addressed. Heterogeneity may be defined as diversity among the nodes in resources or capability. The environment is usually heterogeneous in case of constraint fluid dynamic environment of MANET. In this paper we are analyzing the routing performance as well as energy efficient behavior of AODV routing protocol in both homogeneous and heterogeneous MANETs (H-MANETs), using performance parameters like ratio of delivered packets, throughput, average delay, average power consumption, energy of alive nodes, etc. Heterogeneity has been introduced in terms of different initial energy for all the nodes, unlike the homogeneous scenario. The simulation work has been done using network simulator (NS-2). This work will be helpful to get insight of effects of heterogeneity on energy efficiency and other performance metrics of AODV.

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Crop diseases are a major cause of reduced productivity in India, with farmers often struggling to identify and control them. Consequently, the development of advanced techniques for early disease detection is crucial for minimizing losses. This study investigates the performance of various Machine Learning (ML) algorithms, including Random Forest (RF), AdaBoost, Gradient Boosting (GB), and Multi-Layer Perceptron (MLP), for predicting diseases in chili crops based on images. The primary objective is to identify the most accurate model for chili crop disease prediction. A novel dataset, the Real Chili Crop Field Image Dataset, comprising approximately 1157 images across 5 distinct classes, is employed for this purpose. The experimental results demonstrate that the RF and GB algorithms achieve accuracies of 96% and 94%, respectively. Importantly, the study focuses on the Real Chili Crop Field Image Dataset, which offers significant advantages in terms of real-world applicability due to its development in natural, non-controlled environments. The methodology is further enhanced by employing popular and diverse feature extraction methods, such as Haralick and Hu moments, and improving the results using the Random Forest classification algorithm.